Data Engineer Training
Toronto, Canada

What is a Data Engineer?

A data engineer conceives, builds and maintains the data infrastructure that holds your enterprise’s advanced analytics capacities together.

A data engineer is responsible for building and maintaining the data architecture of a data science project. Data Engineers are responsible for the creation and maintenance of analytics infrastructure that enables almost every other function in the data world. They are responsible for the development, construction, maintenance and testing of architectures, such as databases and large-scale processing systems. As part of this, Data Engineers are also responsible for the creation of data set processes used in modeling, mining, acquisition, and verification.

 What is Data Engineering

Data engineering is a software engineering practice with a focus on design, development, and productionizing of data processing systems. Data processing includes all the practical aspects of data handling, including:Data acquisition, transfer, transformation, and storage on-prem or in the cloud. In many cases, data can be categorized as Big Data.

Gartner’s Definition of Big Data

Gartner’s analyst Doug Laney defined three dimensions to data growth challenges: increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources).

In 2012, Gartner updated its definition as follows: “Big data are high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.”

Volume

Data sizes accumulated in many organizations come to hundreds of terabytes, approaching the petabyte levels.

Variety

Big Data comes in different formats as well as unformatted (unstructured) and various types like text, audio, voice, VoIP, images, video, e-mails, web traffic log files entries, sensor byte streams, etc.

Velocity

High traffic on-line banking web site can generate hundreds of TPS (transactions per second) each of which may be required to be subjected to fraud detection analysis in real or near-real time.

What is the difference between a Data Scientist and a Data Engineer?

Broadly speaking, a data scientist builds models using a combination of statistics, mathematics, machine learning and domain based knowledge. He/she has to code and build these models using the same tools/languages and framework that the organization supports.

A data engineer on the other hand has to build and maintain data structures and architectures for data ingestion, processing, and deployment for large-scale data-intensive applications. To build a pipeline for data collection and storage, to funnel the data to the data scientists, to put the model into production – these are just some of the tasks a data engineer has to perform.

Data scientists and data engineers need to work together for any large scale data science project to suc

What are the core Data Engineering skills?

• Introduction to Data Engineering (Related Course: Data Engineering Training for Managers)
• Basic Language Requirement: Python (Related Course: Introduction to Python Programming)
• Solid Knowledge of Operating Systems (Related Courses: Unix Training)
• Heavy, In-Depth Database Knowledge – SQL and NoSQL (Related Courses: NoSQL Training and Courseware)
• Data Warehousing – Hadoop, MapReduce, HIVE, PIG, Apache Spark, Kafka (Related Courses: Big Data Training)
• Basic Machine Learning Familiarity (Related Courses: AI and Machine Learning Training)